Dataset statistics
| Number of variables | 18 |
|---|---|
| Number of observations | 525 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 74.0 KiB |
| Average record size in memory | 144.2 B |
Variable types
| Numeric | 8 |
|---|---|
| Categorical | 6 |
| Boolean | 4 |
Certificate has constant value "True" | Constant |
Trainer_experiance is highly correlated with Course_hours and 6 other fields | High correlation |
Course_hours is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Course_rating is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Rental_permises is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Trainer_slary is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Maintaince_cost is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Non_teaching_staff_salary is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Price is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Trainer_experiance is highly correlated with Course_hours and 6 other fields | High correlation |
Course_hours is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Course_rating is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Rental_permises is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Trainer_slary is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Maintaince_cost is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Non_teaching_staff_salary is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Price is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Trainer_experiance is highly correlated with Course_hours and 6 other fields | High correlation |
Course_hours is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Course_rating is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Rental_permises is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Trainer_slary is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Maintaince_cost is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Non_teaching_staff_salary is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Price is highly correlated with Trainer_experiance and 6 other fields | High correlation |
Certificate is highly correlated with Location and 8 other fields | High correlation |
Location is highly correlated with Certificate | High correlation |
Offline_classes is highly correlated with Certificate | High correlation |
Course_rating is highly correlated with Certificate | High correlation |
Subject is highly correlated with Certificate | High correlation |
Placements is highly correlated with Certificate | High correlation |
Online_classes is highly correlated with Certificate | High correlation |
Institute is highly correlated with Certificate | High correlation |
Course_level is highly correlated with Certificate | High correlation |
Trainer_Qualification is highly correlated with Certificate | High correlation |
Course_hours is highly correlated with Course_rating and 6 other fields | High correlation |
Course_rating is highly correlated with Course_hours and 6 other fields | High correlation |
Maintaince_cost is highly correlated with Course_hours and 6 other fields | High correlation |
Non_teaching_staff_salary is highly correlated with Course_hours and 6 other fields | High correlation |
Price is highly correlated with Course_hours and 6 other fields | High correlation |
Rental_permises is highly correlated with Course_hours and 6 other fields | High correlation |
Trainer_experiance is highly correlated with Course_hours and 6 other fields | High correlation |
Trainer_slary is highly correlated with Course_hours and 6 other fields | High correlation |
s.no is uniformly distributed | Uniform |
s.no has unique values | Unique |
Reproduction
| Analysis started | 2022-07-01 05:03:37.752292 |
|---|---|
| Analysis finished | 2022-07-01 05:03:46.381936 |
| Duration | 8.63 seconds |
| Software version | pandas-profiling v3.2.0 |
| Download configuration | config.json |
| Distinct | 525 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 263 |
| Minimum | 1 |
|---|---|
| Maximum | 525 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 27.2 |
| Q1 | 132 |
| median | 263 |
| Q3 | 394 |
| 95-th percentile | 498.8 |
| Maximum | 525 |
| Range | 524 |
| Interquartile range (IQR) | 262 |
Descriptive statistics
| Standard deviation | 151.6987146 |
|---|---|
| Coefficient of variation (CV) | 0.5768011961 |
| Kurtosis | -1.2 |
| Mean | 263 |
| Median Absolute Deviation (MAD) | 131 |
| Skewness | 0 |
| Sum | 138075 |
| Variance | 23012.5 |
| Monotonicity | Strictly increasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1 | 1 | 0.2% |
| 362 | 1 | 0.2% |
| 360 | 1 | 0.2% |
| 359 | 1 | 0.2% |
| 358 | 1 | 0.2% |
| 357 | 1 | 0.2% |
| 356 | 1 | 0.2% |
| 355 | 1 | 0.2% |
| 354 | 1 | 0.2% |
| 353 | 1 | 0.2% |
| Other values (515) | 515 |
| Value | Count | Frequency (%) |
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 1 |
| Value | Count | Frequency (%) |
| 525 | 1 | |
| 524 | 1 | |
| 523 | 1 | |
| 522 | 1 | |
| 521 | 1 | |
| 520 | 1 | |
| 519 | 1 | |
| 518 | 1 | |
| 517 | 1 | |
| 516 | 1 |
| Distinct | 14 |
|---|---|
| Distinct (%) | 2.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 4.2 KiB |
| innomatics | |
|---|---|
| Great_Learn | |
| Coursera | |
| Datatrain | |
| Upgrad | |
| Other values (9) |
Length
| Max length | 12 |
|---|---|
| Median length | 9 |
| Mean length | 7.866666667 |
| Min length | 3 |
Characters and Unicode
| Total characters | 4130 |
|---|---|
| Distinct characters | 33 |
| Distinct categories | 4 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Datatrain |
|---|---|
| 2nd row | Datatrain |
| 3rd row | Guvi |
| 4th row | innomatics |
| 5th row | Datatrain |
Common Values
| Value | Count | Frequency (%) |
| innomatics | 49 | 9.3% |
| Great_Learn | 46 | 8.8% |
| Coursera | 45 | 8.6% |
| Datatrain | 41 | 7.8% |
| Upgrad | 41 | 7.8% |
| ExcelR | 40 | 7.6% |
| Simple_learn | 36 | 6.9% |
| Udemy | 35 | 6.7% |
| Edvancer | 34 | 6.5% |
| Edureka | 33 | 6.3% |
| Other values (4) | 125 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| innomatics | 49 | 9.3% |
| great_learn | 46 | 8.8% |
| coursera | 45 | 8.6% |
| datatrain | 41 | 7.8% |
| upgrad | 41 | 7.8% |
| excelr | 40 | 7.6% |
| simple_learn | 36 | 6.9% |
| udemy | 35 | 6.7% |
| edvancer | 34 | 6.5% |
| edureka | 33 | 6.3% |
| Other values (4) | 125 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 549 | 13.3% |
| r | 367 | 8.9% |
| e | 351 | 8.5% |
| i | 267 | 6.5% |
| n | 255 | 6.2% |
| t | 209 | 5.1% |
| d | 174 | 4.2% |
| c | 155 | 3.8% |
| m | 152 | 3.7% |
| E | 138 | 3.3% |
| Other values (23) | 1513 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 3274 | |
| Uppercase Letter | 652 | 15.8% |
| Connector Punctuation | 114 | 2.8% |
| Decimal Number | 90 | 2.2% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 549 | |
| r | 367 | |
| e | 351 | |
| i | 267 | 8.2% |
| n | 255 | 7.8% |
| t | 209 | 6.4% |
| d | 174 | 5.3% |
| c | 155 | 4.7% |
| m | 152 | 4.6% |
| l | 112 | 3.4% |
| Other values (9) | 683 |
Uppercase Letter
| Value | Count | Frequency (%) |
| E | 138 | |
| G | 108 | |
| D | 103 | |
| U | 76 | |
| L | 46 | 7.1% |
| C | 45 | 6.9% |
| R | 40 | 6.1% |
| S | 36 | 5.5% |
| T | 30 | 4.6% |
| M | 30 | 4.6% |
Decimal Number
| Value | Count | Frequency (%) |
| 3 | 30 | |
| 6 | 30 | |
| 0 | 30 |
Connector Punctuation
| Value | Count | Frequency (%) |
| _ | 114 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 3926 | |
| Common | 204 | 4.9% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 549 | |
| r | 367 | 9.3% |
| e | 351 | 8.9% |
| i | 267 | 6.8% |
| n | 255 | 6.5% |
| t | 209 | 5.3% |
| d | 174 | 4.4% |
| c | 155 | 3.9% |
| m | 152 | 3.9% |
| E | 138 | 3.5% |
| Other values (19) | 1309 |
Common
| Value | Count | Frequency (%) |
| _ | 114 | |
| 3 | 30 | 14.7% |
| 6 | 30 | 14.7% |
| 0 | 30 | 14.7% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 4130 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| a | 549 | 13.3% |
| r | 367 | 8.9% |
| e | 351 | 8.5% |
| i | 267 | 6.5% |
| n | 255 | 6.2% |
| t | 209 | 5.1% |
| d | 174 | 4.2% |
| c | 155 | 3.8% |
| m | 152 | 3.7% |
| E | 138 | 3.3% |
| Other values (23) | 1513 |
| Distinct | 10 |
|---|---|
| Distinct (%) | 1.9% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 4.2 KiB |
| ProjectManagement | |
|---|---|
| python | |
| DataScience | |
| ArtificialIntelligence | |
| FullstackDataScience | |
| Other values (5) |
Length
| Max length | 22 |
|---|---|
| Median length | 16 |
| Mean length | 14.44571429 |
| Min length | 6 |
Characters and Unicode
| Total characters | 7584 |
|---|---|
| Distinct characters | 30 |
| Distinct categories | 2 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | ProjectManagement |
|---|---|
| 2nd row | DataScience |
| 3rd row | python |
| 4th row | DigitalTransformation |
| 5th row | DigitalTransformation |
Common Values
| Value | Count | Frequency (%) |
| ProjectManagement | 62 | |
| python | 60 | |
| DataScience | 58 | |
| ArtificialIntelligence | 53 | |
| FullstackDataScience | 53 | |
| BigData | 52 | |
| CloudComputing | 52 | |
| DigitalTransformation | 46 | |
| DigitalMarketing | 45 | |
| Dataanalysis | 44 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| projectmanagement | 62 | |
| python | 60 | |
| datascience | 58 | |
| artificialintelligence | 53 | |
| fullstackdatascience | 53 | |
| bigdata | 52 | |
| cloudcomputing | 52 | |
| digitaltransformation | 46 | |
| digitalmarketing | 45 | |
| dataanalysis | 44 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 960 | |
| t | 784 | 10.3% |
| i | 744 | 9.8% |
| n | 634 | 8.4% |
| e | 612 | 8.1% |
| l | 452 | 6.0% |
| c | 443 | 5.8% |
| g | 355 | 4.7% |
| o | 318 | 4.2% |
| D | 298 | 3.9% |
| Other values (20) | 1984 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 6645 | |
| Uppercase Letter | 939 | 12.4% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 960 | |
| t | 784 | |
| i | 744 | |
| n | 634 | |
| e | 612 | |
| l | 452 | |
| c | 443 | |
| g | 355 | 5.3% |
| o | 318 | 4.8% |
| r | 252 | 3.8% |
| Other values (10) | 1091 |
Uppercase Letter
| Value | Count | Frequency (%) |
| D | 298 | |
| S | 111 | 11.8% |
| M | 107 | 11.4% |
| C | 104 | 11.1% |
| P | 62 | 6.6% |
| F | 53 | 5.6% |
| I | 53 | 5.6% |
| A | 53 | 5.6% |
| B | 52 | 5.5% |
| T | 46 | 4.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 7584 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 960 | |
| t | 784 | 10.3% |
| i | 744 | 9.8% |
| n | 634 | 8.4% |
| e | 612 | 8.1% |
| l | 452 | 6.0% |
| c | 443 | 5.8% |
| g | 355 | 4.7% |
| o | 318 | 4.2% |
| D | 298 | 3.9% |
| Other values (20) | 1984 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 7584 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| a | 960 | |
| t | 784 | 10.3% |
| i | 744 | 9.8% |
| n | 634 | 8.4% |
| e | 612 | 8.1% |
| l | 452 | 6.0% |
| c | 443 | 5.8% |
| g | 355 | 4.7% |
| o | 318 | 4.2% |
| D | 298 | 3.9% |
| Other values (20) | 1984 |
| Distinct | 9 |
|---|---|
| Distinct (%) | 1.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 4.2 KiB |
| Bengaluru | |
|---|---|
| Hyderabad | |
| Chennai | |
| Ahmedabad | |
| Pune | |
| Other values (4) |
Length
| Max length | 9 |
|---|---|
| Median length | 8 |
| Mean length | 7.161904762 |
| Min length | 4 |
Characters and Unicode
| Total characters | 3760 |
|---|---|
| Distinct characters | 25 |
| Distinct categories | 2 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Delhi |
|---|---|
| 2nd row | Hyderabad |
| 3rd row | Chennai |
| 4th row | Ahmedabad |
| 5th row | Pune |
Common Values
| Value | Count | Frequency (%) |
| Bengaluru | 65 | |
| Hyderabad | 63 | |
| Chennai | 61 | |
| Ahmedabad | 61 | |
| Pune | 61 | |
| Delhi | 57 | |
| Calcutta | 54 | |
| Kanpour | 53 | |
| Mumbai | 50 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| bengaluru | 65 | |
| hyderabad | 63 | |
| chennai | 61 | |
| ahmedabad | 61 | |
| pune | 61 | |
| delhi | 57 | |
| calcutta | 54 | |
| kanpour | 53 | |
| mumbai | 50 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 585 | |
| e | 368 | 9.8% |
| u | 348 | 9.3% |
| n | 301 | 8.0% |
| d | 248 | 6.6% |
| r | 181 | 4.8% |
| h | 179 | 4.8% |
| l | 176 | 4.7% |
| b | 174 | 4.6% |
| i | 168 | 4.5% |
| Other values (15) | 1032 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 3235 | |
| Uppercase Letter | 525 | 14.0% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 585 | |
| e | 368 | |
| u | 348 | |
| n | 301 | |
| d | 248 | |
| r | 181 | 5.6% |
| h | 179 | 5.5% |
| l | 176 | 5.4% |
| b | 174 | 5.4% |
| i | 168 | 5.2% |
| Other values (7) | 507 |
Uppercase Letter
| Value | Count | Frequency (%) |
| C | 115 | |
| B | 65 | |
| H | 63 | |
| A | 61 | |
| P | 61 | |
| D | 57 | |
| K | 53 | |
| M | 50 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 3760 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 585 | |
| e | 368 | 9.8% |
| u | 348 | 9.3% |
| n | 301 | 8.0% |
| d | 248 | 6.6% |
| r | 181 | 4.8% |
| h | 179 | 4.8% |
| l | 176 | 4.7% |
| b | 174 | 4.6% |
| i | 168 | 4.5% |
| Other values (15) | 1032 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 3760 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| a | 585 | |
| e | 368 | 9.8% |
| u | 348 | 9.3% |
| n | 301 | 8.0% |
| d | 248 | 6.6% |
| r | 181 | 4.8% |
| h | 179 | 4.8% |
| l | 176 | 4.7% |
| b | 174 | 4.6% |
| i | 168 | 4.5% |
| Other values (15) | 1032 |
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 4.2 KiB |
| Graduate | |
|---|---|
| Doctorate | |
| UnderGraduate |
Length
| Max length | 13 |
|---|---|
| Median length | 9 |
| Mean length | 9.891428571 |
| Min length | 8 |
Characters and Unicode
| Total characters | 5193 |
|---|---|
| Distinct characters | 12 |
| Distinct categories | 2 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Doctorate |
|---|---|
| 2nd row | Graduate |
| 3rd row | UnderGraduate |
| 4th row | Doctorate |
| 5th row | UnderGraduate |
Common Values
| Value | Count | Frequency (%) |
| Graduate | 184 | |
| Doctorate | 178 | |
| UnderGraduate | 163 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| graduate | 184 | |
| doctorate | 178 | |
| undergraduate | 163 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 872 | |
| t | 703 | |
| r | 688 | |
| e | 688 | |
| d | 510 | |
| o | 356 | |
| G | 347 | 6.7% |
| u | 347 | 6.7% |
| D | 178 | 3.4% |
| c | 178 | 3.4% |
| Other values (2) | 326 | 6.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 4505 | |
| Uppercase Letter | 688 | 13.2% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 872 | |
| t | 703 | |
| r | 688 | |
| e | 688 | |
| d | 510 | |
| o | 356 | |
| u | 347 | 7.7% |
| c | 178 | 4.0% |
| n | 163 | 3.6% |
Uppercase Letter
| Value | Count | Frequency (%) |
| G | 347 | |
| D | 178 | |
| U | 163 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 5193 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 872 | |
| t | 703 | |
| r | 688 | |
| e | 688 | |
| d | 510 | |
| o | 356 | |
| G | 347 | 6.7% |
| u | 347 | 6.7% |
| D | 178 | 3.4% |
| c | 178 | 3.4% |
| Other values (2) | 326 | 6.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 5193 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| a | 872 | |
| t | 703 | |
| r | 688 | |
| e | 688 | |
| d | 510 | |
| o | 356 | |
| G | 347 | 6.7% |
| u | 347 | 6.7% |
| D | 178 | 3.4% |
| c | 178 | 3.4% |
| Other values (2) | 326 | 6.3% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 653.0 B |
| True | |
|---|---|
| False |
| Value | Count | Frequency (%) |
| True | 290 | |
| False | 235 |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 653.0 B |
| True | |
|---|---|
| False |
| Value | Count | Frequency (%) |
| True | 275 | |
| False | 250 |
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 4.2 KiB |
| intermediate | |
|---|---|
| beginer | |
| advanced |
Length
| Max length | 12 |
|---|---|
| Median length | 8 |
| Mean length | 9.036190476 |
| Min length | 7 |
Characters and Unicode
| Total characters | 4744 |
|---|---|
| Distinct characters | 12 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | advanced |
|---|---|
| 2nd row | advanced |
| 3rd row | advanced |
| 4th row | intermediate |
| 5th row | advanced |
Common Values
| Value | Count | Frequency (%) |
| intermediate | 181 | |
| beginer | 180 | |
| advanced | 164 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| intermediate | 181 | |
| beginer | 180 | |
| advanced | 164 |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 1067 | |
| i | 542 | |
| n | 525 | |
| d | 509 | |
| a | 509 | |
| t | 362 | 7.6% |
| r | 361 | 7.6% |
| m | 181 | 3.8% |
| b | 180 | 3.8% |
| g | 180 | 3.8% |
| Other values (2) | 328 | 6.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 4744 |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 1067 | |
| i | 542 | |
| n | 525 | |
| d | 509 | |
| a | 509 | |
| t | 362 | 7.6% |
| r | 361 | 7.6% |
| m | 181 | 3.8% |
| b | 180 | 3.8% |
| g | 180 | 3.8% |
| Other values (2) | 328 | 6.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 4744 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 1067 | |
| i | 542 | |
| n | 525 | |
| d | 509 | |
| a | 509 | |
| t | 362 | 7.6% |
| r | 361 | 7.6% |
| m | 181 | 3.8% |
| b | 180 | 3.8% |
| g | 180 | 3.8% |
| Other values (2) | 328 | 6.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 4744 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| e | 1067 | |
| i | 542 | |
| n | 525 | |
| d | 509 | |
| a | 509 | |
| t | 362 | 7.6% |
| r | 361 | 7.6% |
| m | 181 | 3.8% |
| b | 180 | 3.8% |
| g | 180 | 3.8% |
| Other values (2) | 328 | 6.9% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 653.0 B |
| False | |
|---|---|
| True |
| Value | Count | Frequency (%) |
| False | 269 | |
| True | 256 |
| Distinct | 1 |
|---|---|
| Distinct (%) | 0.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 653.0 B |
| True |
|---|
| Value | Count | Frequency (%) |
| True | 525 |
| Distinct | 20 |
|---|---|
| Distinct (%) | 3.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 10.79047619 |
| Minimum | 1 |
|---|---|
| Maximum | 20 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 2 |
| Q1 | 6 |
| median | 11 |
| Q3 | 15 |
| 95-th percentile | 19 |
| Maximum | 20 |
| Range | 19 |
| Interquartile range (IQR) | 9 |
Descriptive statistics
| Standard deviation | 5.515929808 |
|---|---|
| Coefficient of variation (CV) | 0.5111850219 |
| Kurtosis | -1.07758173 |
| Mean | 10.79047619 |
| Median Absolute Deviation (MAD) | 5 |
| Skewness | -0.07460615264 |
| Sum | 5665 |
| Variance | 30.42548164 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=20)
| Value | Count | Frequency (%) |
| 13 | 42 | 8.0% |
| 11 | 42 | 8.0% |
| 6 | 38 | 7.2% |
| 19 | 28 | 5.3% |
| 18 | 28 | 5.3% |
| 9 | 28 | 5.3% |
| 16 | 27 | 5.1% |
| 12 | 27 | 5.1% |
| 17 | 27 | 5.1% |
| 10 | 26 | 5.0% |
| Other values (10) | 212 |
| Value | Count | Frequency (%) |
| 1 | 22 | |
| 2 | 24 | |
| 3 | 19 | |
| 4 | 17 | |
| 5 | 25 | |
| 6 | 38 | |
| 7 | 22 | |
| 8 | 20 | |
| 9 | 28 | |
| 10 | 26 |
| Value | Count | Frequency (%) |
| 20 | 21 | |
| 19 | 28 | |
| 18 | 28 | |
| 17 | 27 | |
| 16 | 27 | |
| 15 | 24 | |
| 14 | 18 | |
| 13 | 42 | |
| 12 | 27 | |
| 11 | 42 |
| Distinct | 150 |
|---|---|
| Distinct (%) | 28.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 119.5638095 |
| Minimum | 40 |
|---|---|
| Maximum | 200 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 40 |
|---|---|
| 5-th percentile | 47 |
| Q1 | 78 |
| median | 118 |
| Q3 | 165 |
| 95-th percentile | 192 |
| Maximum | 200 |
| Range | 160 |
| Interquartile range (IQR) | 87 |
Descriptive statistics
| Standard deviation | 47.49260916 |
|---|---|
| Coefficient of variation (CV) | 0.3972155901 |
| Kurtosis | -1.217647116 |
| Mean | 119.5638095 |
| Median Absolute Deviation (MAD) | 43 |
| Skewness | 0.03513412236 |
| Sum | 62771 |
| Variance | 2255.547924 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 127 | 9 | 1.7% |
| 42 | 9 | 1.7% |
| 51 | 8 | 1.5% |
| 182 | 8 | 1.5% |
| 178 | 8 | 1.5% |
| 118 | 7 | 1.3% |
| 62 | 7 | 1.3% |
| 74 | 7 | 1.3% |
| 97 | 7 | 1.3% |
| 109 | 7 | 1.3% |
| Other values (140) | 448 |
| Value | Count | Frequency (%) |
| 40 | 2 | 0.4% |
| 41 | 3 | 0.6% |
| 42 | 9 | |
| 43 | 4 | |
| 44 | 3 | 0.6% |
| 45 | 1 | 0.2% |
| 46 | 3 | 0.6% |
| 47 | 4 | |
| 48 | 3 | 0.6% |
| 49 | 5 |
| Value | Count | Frequency (%) |
| 200 | 3 | |
| 199 | 3 | |
| 198 | 3 | |
| 197 | 1 | 0.2% |
| 196 | 7 | |
| 195 | 3 | |
| 194 | 3 | |
| 193 | 2 | 0.4% |
| 192 | 6 | |
| 191 | 6 |
Course_rating
Categorical
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATION| Distinct | 5 |
|---|---|
| Distinct (%) | 1.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 4.2 KiB |
| 3 | |
|---|---|
| 2 | |
| 4 | |
| 1 | |
| 5 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 525 |
|---|---|
| Distinct characters | 5 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 5 |
|---|---|
| 2nd row | 2 |
| 3rd row | 4 |
| 4th row | 5 |
| 5th row | 2 |
Common Values
| Value | Count | Frequency (%) |
| 3 | 150 | |
| 2 | 120 | |
| 4 | 120 | |
| 1 | 78 | |
| 5 | 57 | 10.9% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 3 | 150 | |
| 2 | 120 | |
| 4 | 120 | |
| 1 | 78 | |
| 5 | 57 | 10.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| 3 | 150 | |
| 2 | 120 | |
| 4 | 120 | |
| 1 | 78 | |
| 5 | 57 | 10.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 525 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 3 | 150 | |
| 2 | 120 | |
| 4 | 120 | |
| 1 | 78 | |
| 5 | 57 | 10.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 525 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 3 | 150 | |
| 2 | 120 | |
| 4 | 120 | |
| 1 | 78 | |
| 5 | 57 | 10.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 525 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 3 | 150 | |
| 2 | 120 | |
| 4 | 120 | |
| 1 | 78 | |
| 5 | 57 | 10.9% |
| Distinct | 134 |
|---|---|
| Distinct (%) | 25.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 82.63238095 |
| Minimum | 15 |
|---|---|
| Maximum | 150 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 15 |
|---|---|
| 5-th percentile | 20 |
| Q1 | 49 |
| median | 84 |
| Q3 | 118 |
| 95-th percentile | 142.8 |
| Maximum | 150 |
| Range | 135 |
| Interquartile range (IQR) | 69 |
Descriptive statistics
| Standard deviation | 39.1478907 |
|---|---|
| Coefficient of variation (CV) | 0.4737596842 |
| Kurtosis | -1.145711474 |
| Mean | 82.63238095 |
| Median Absolute Deviation (MAD) | 34 |
| Skewness | -0.03454047398 |
| Sum | 43382 |
| Variance | 1532.557346 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 84 | 12 | 2.3% |
| 131 | 10 | 1.9% |
| 16 | 10 | 1.9% |
| 75 | 9 | 1.7% |
| 141 | 8 | 1.5% |
| 95 | 8 | 1.5% |
| 34 | 8 | 1.5% |
| 78 | 8 | 1.5% |
| 62 | 8 | 1.5% |
| 90 | 7 | 1.3% |
| Other values (124) | 437 |
| Value | Count | Frequency (%) |
| 15 | 5 | |
| 16 | 10 | |
| 17 | 7 | |
| 18 | 3 | 0.6% |
| 19 | 1 | 0.2% |
| 20 | 2 | 0.4% |
| 21 | 4 | 0.8% |
| 22 | 4 | 0.8% |
| 23 | 2 | 0.4% |
| 24 | 7 |
| Value | Count | Frequency (%) |
| 150 | 2 | 0.4% |
| 149 | 3 | 0.6% |
| 148 | 4 | |
| 147 | 4 | |
| 146 | 3 | 0.6% |
| 145 | 7 | |
| 144 | 2 | 0.4% |
| 143 | 2 | 0.4% |
| 142 | 2 | 0.4% |
| 141 | 8 |
| Distinct | 313 |
|---|---|
| Distinct (%) | 59.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 506.112381 |
| Minimum | 200 |
|---|---|
| Maximum | 800 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 200 |
|---|---|
| 5-th percentile | 230 |
| Q1 | 354 |
| median | 515 |
| Q3 | 653 |
| 95-th percentile | 773.8 |
| Maximum | 800 |
| Range | 600 |
| Interquartile range (IQR) | 299 |
Descriptive statistics
| Standard deviation | 174.4551298 |
|---|---|
| Coefficient of variation (CV) | 0.3446964278 |
| Kurtosis | -1.164841365 |
| Mean | 506.112381 |
| Median Absolute Deviation (MAD) | 155 |
| Skewness | -0.05501038188 |
| Sum | 265709 |
| Variance | 30434.59231 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 511 | 8 | 1.5% |
| 246 | 6 | 1.1% |
| 290 | 6 | 1.1% |
| 720 | 5 | 1.0% |
| 520 | 4 | 0.8% |
| 230 | 4 | 0.8% |
| 280 | 4 | 0.8% |
| 539 | 4 | 0.8% |
| 545 | 4 | 0.8% |
| 210 | 4 | 0.8% |
| Other values (303) | 476 |
| Value | Count | Frequency (%) |
| 200 | 1 | 0.2% |
| 202 | 1 | 0.2% |
| 204 | 2 | |
| 205 | 1 | 0.2% |
| 207 | 1 | 0.2% |
| 208 | 1 | 0.2% |
| 209 | 1 | 0.2% |
| 210 | 4 | |
| 211 | 1 | 0.2% |
| 212 | 1 | 0.2% |
| Value | Count | Frequency (%) |
| 800 | 3 | |
| 799 | 2 | |
| 798 | 1 | 0.2% |
| 797 | 1 | 0.2% |
| 796 | 3 | |
| 795 | 2 | |
| 794 | 2 | |
| 791 | 1 | 0.2% |
| 789 | 2 | |
| 787 | 2 |
| Distinct | 132 |
|---|---|
| Distinct (%) | 25.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 84.02095238 |
| Minimum | 15 |
|---|---|
| Maximum | 150 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 15 |
|---|---|
| 5-th percentile | 20.2 |
| Q1 | 51 |
| median | 85 |
| Q3 | 119 |
| 95-th percentile | 143 |
| Maximum | 150 |
| Range | 135 |
| Interquartile range (IQR) | 68 |
Descriptive statistics
| Standard deviation | 38.97583832 |
|---|---|
| Coefficient of variation (CV) | 0.4638823676 |
| Kurtosis | -1.117248825 |
| Mean | 84.02095238 |
| Median Absolute Deviation (MAD) | 34 |
| Skewness | -0.05983190389 |
| Sum | 44111 |
| Variance | 1519.115972 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 132 | 11 | 2.1% |
| 16 | 11 | 2.1% |
| 35 | 9 | 1.7% |
| 25 | 9 | 1.7% |
| 76 | 9 | 1.7% |
| 96 | 8 | 1.5% |
| 47 | 8 | 1.5% |
| 80 | 8 | 1.5% |
| 63 | 8 | 1.5% |
| 95 | 7 | 1.3% |
| Other values (122) | 437 |
| Value | Count | Frequency (%) |
| 15 | 1 | 0.2% |
| 16 | 11 | |
| 17 | 4 | 0.8% |
| 18 | 5 | |
| 19 | 3 | 0.6% |
| 20 | 3 | 0.6% |
| 21 | 3 | 0.6% |
| 22 | 2 | 0.4% |
| 23 | 4 | 0.8% |
| 24 | 1 | 0.2% |
| Value | Count | Frequency (%) |
| 150 | 3 | |
| 149 | 7 | |
| 148 | 4 | |
| 147 | 3 | |
| 146 | 4 | |
| 145 | 3 | |
| 144 | 2 | 0.4% |
| 143 | 4 | |
| 142 | 7 | |
| 141 | 4 |
Non_teaching_staff_salary
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATION| Distinct | 164 |
|---|---|
| Distinct (%) | 31.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 136.6152381 |
| Minimum | 50 |
|---|---|
| Maximum | 225 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 50 |
|---|---|
| 5-th percentile | 59 |
| Q1 | 94 |
| median | 137 |
| Q3 | 181 |
| 95-th percentile | 217 |
| Maximum | 225 |
| Range | 175 |
| Interquartile range (IQR) | 87 |
Descriptive statistics
| Standard deviation | 50.66824965 |
|---|---|
| Coefficient of variation (CV) | 0.3708828558 |
| Kurtosis | -1.133297419 |
| Mean | 136.6152381 |
| Median Absolute Deviation (MAD) | 44 |
| Skewness | 0.02271543259 |
| Sum | 71723 |
| Variance | 2567.271523 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 75 | 8 | 1.5% |
| 146 | 7 | 1.3% |
| 224 | 7 | 1.3% |
| 52 | 7 | 1.3% |
| 72 | 7 | 1.3% |
| 151 | 7 | 1.3% |
| 63 | 7 | 1.3% |
| 127 | 7 | 1.3% |
| 95 | 6 | 1.1% |
| 116 | 6 | 1.1% |
| Other values (154) | 456 |
| Value | Count | Frequency (%) |
| 50 | 2 | 0.4% |
| 51 | 4 | |
| 52 | 7 | |
| 53 | 2 | 0.4% |
| 54 | 4 | |
| 55 | 2 | 0.4% |
| 56 | 1 | 0.2% |
| 57 | 2 | 0.4% |
| 58 | 2 | 0.4% |
| 59 | 4 |
| Value | Count | Frequency (%) |
| 225 | 4 | |
| 224 | 7 | |
| 223 | 3 | |
| 222 | 1 | 0.2% |
| 221 | 4 | |
| 220 | 2 | 0.4% |
| 219 | 4 | |
| 217 | 3 | |
| 216 | 1 | 0.2% |
| 215 | 5 |
| Distinct | 428 |
|---|---|
| Distinct (%) | 81.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 895.992381 |
| Minimum | 302 |
|---|---|
| Maximum | 1499 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 4.2 KiB |
Quantile statistics
| Minimum | 302 |
|---|---|
| 5-th percentile | 363.2 |
| Q1 | 602 |
| median | 889 |
| Q3 | 1192 |
| 95-th percentile | 1445.4 |
| Maximum | 1499 |
| Range | 1197 |
| Interquartile range (IQR) | 590 |
Descriptive statistics
| Standard deviation | 343.817128 |
|---|---|
| Coefficient of variation (CV) | 0.3837277362 |
| Kurtosis | -1.097880353 |
| Mean | 895.992381 |
| Median Absolute Deviation (MAD) | 291 |
| Skewness | 0.03731601394 |
| Sum | 470396 |
| Variance | 118210.2175 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1495 | 5 | 1.0% |
| 474 | 4 | 0.8% |
| 1167 | 4 | 0.8% |
| 796 | 3 | 0.6% |
| 745 | 3 | 0.6% |
| 946 | 3 | 0.6% |
| 932 | 3 | 0.6% |
| 320 | 3 | 0.6% |
| 928 | 3 | 0.6% |
| 1318 | 3 | 0.6% |
| Other values (418) | 491 |
| Value | Count | Frequency (%) |
| 302 | 1 | 0.2% |
| 306 | 1 | 0.2% |
| 307 | 2 | |
| 308 | 2 | |
| 310 | 1 | 0.2% |
| 311 | 1 | 0.2% |
| 318 | 1 | 0.2% |
| 319 | 1 | 0.2% |
| 320 | 3 | |
| 322 | 1 | 0.2% |
| Value | Count | Frequency (%) |
| 1499 | 1 | 0.2% |
| 1498 | 1 | 0.2% |
| 1497 | 1 | 0.2% |
| 1496 | 2 | 0.4% |
| 1495 | 5 | |
| 1494 | 1 | 0.2% |
| 1492 | 1 | 0.2% |
| 1487 | 1 | 0.2% |
| 1484 | 1 | 0.2% |
| 1482 | 1 | 0.2% |
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Cramér's V (φc)
Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here. A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
First rows
| s.no | Institute | Subject | Location | Trainer_Qualification | Online_classes | Offline_classes | Course_level | Placements | Certificate | Trainer_experiance | Course_hours | Course_rating | Rental_permises | Trainer_slary | Maintaince_cost | Non_teaching_staff_salary | Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Datatrain | ProjectManagement | Delhi | Doctorate | Yes | Yes | advanced | Yes | Yes | 19 | 192 | 5 | 145 | 722 | 140 | 205 | 1419 |
| 1 | 2 | Datatrain | DataScience | Hyderabad | Graduate | Yes | no | advanced | No | Yes | 9 | 94 | 2 | 68 | 454 | 70 | 116 | 746 |
| 2 | 3 | Guvi | python | Chennai | UnderGraduate | no | no | advanced | Yes | Yes | 15 | 154 | 4 | 112 | 641 | 118 | 179 | 1121 |
| 3 | 4 | innomatics | DigitalTransformation | Ahmedabad | Doctorate | Yes | Yes | intermediate | No | Yes | 18 | 182 | 5 | 130 | 729 | 132 | 108 | 1341 |
| 4 | 5 | Datatrain | DigitalTransformation | Pune | UnderGraduate | Yes | Yes | advanced | No | Yes | 6 | 75 | 2 | 43 | 349 | 45 | 88 | 512 |
| 5 | 6 | Datatrain | python | Ahmedabad | UnderGraduate | no | Yes | beginer | Yes | Yes | 11 | 115 | 3 | 84 | 510 | 80 | 135 | 879 |
| 6 | 7 | 360DigiTMG | BigData | Pune | UnderGraduate | no | no | beginer | Yes | Yes | 6 | 63 | 2 | 37 | 297 | 40 | 76 | 429 |
| 7 | 8 | Edureka | ArtificialIntelligence | Kanpour | Doctorate | no | Yes | advanced | Yes | Yes | 6 | 70 | 2 | 42 | 315 | 46 | 80 | 481 |
| 8 | 9 | Udemy | BigData | Bengaluru | Doctorate | no | no | beginer | Yes | Yes | 6 | 78 | 2 | 51 | 356 | 51 | 95 | 618 |
| 9 | 10 | Coursera | Dataanalysis | Kanpour | Graduate | Yes | Yes | advanced | Yes | Yes | 6 | 67 | 2 | 39 | 310 | 43 | 78 | 463 |
Last rows
| s.no | Institute | Subject | Location | Trainer_Qualification | Online_classes | Offline_classes | Course_level | Placements | Certificate | Trainer_experiance | Course_hours | Course_rating | Rental_permises | Trainer_slary | Maintaince_cost | Non_teaching_staff_salary | Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 515 | 516 | Guvi | BigData | Pune | Doctorate | no | no | advanced | Yes | Yes | 2 | 49 | 1 | 22 | 238 | 23 | 61 | 377 |
| 516 | 517 | innomatics | DigitalMarketing | Kanpour | UnderGraduate | Yes | no | advanced | No | Yes | 13 | 131 | 3 | 92 | 544 | 94 | 150 | 977 |
| 517 | 518 | Data_camp | Dataanalysis | Hyderabad | UnderGraduate | Yes | no | beginer | No | Yes | 17 | 178 | 4 | 131 | 714 | 132 | 199 | 1318 |
| 518 | 519 | Data_camp | DataScience | Chennai | Doctorate | no | Yes | beginer | Yes | Yes | 17 | 180 | 4 | 135 | 723 | 134 | 202 | 1336 |
| 519 | 520 | innomatics | ProjectManagement | Pune | Graduate | Yes | no | intermediate | No | Yes | 18 | 184 | 5 | 138 | 738 | 137 | 207 | 1371 |
| 520 | 521 | Edureka | CloudComputing | Delhi | Graduate | no | Yes | intermediate | Yes | Yes | 4 | 66 | 1 | 29 | 269 | 30 | 69 | 435 |
| 521 | 522 | Edx | FullstackDataScience | Hyderabad | Doctorate | no | no | beginer | Yes | Yes | 20 | 195 | 5 | 147 | 784 | 146 | 220 | 1462 |
| 522 | 523 | ExcelR | DigitalTransformation | Delhi | Graduate | no | Yes | intermediate | Yes | Yes | 1 | 42 | 1 | 16 | 212 | 16 | 95 | 601 |
| 523 | 524 | Datatrain | CloudComputing | Pune | UnderGraduate | no | no | beginer | No | Yes | 5 | 68 | 1 | 31 | 275 | 32 | 71 | 446 |
| 524 | 525 | Guvi | CloudComputing | Delhi | Graduate | Yes | Yes | beginer | Yes | Yes | 15 | 162 | 4 | 121 | 655 | 120 | 182 | 1187 |